Field of the Invention
The present invention relates to a data updater, and more specifically to a motion video decoder, particularly using distributed video coding (DVC) technologies.
Description of the Background Art
In recent years, the distributed video coding (DVC) technologies have come to attention as a relatively newer coding scheme, as taught by, for example, Anne Aaron et al., “Transform-domain Wyner-Ziv Codec for Video,” Proc. SPIE Visual Communications and Image Processing, San Jose, Calif. (2004).
The DVC scheme is a motion video coding system in which, in a sequence of motion video frames, video frames are intraframe-encoded every several frames while, with respect to the remaining, or possibly entire frames, only error correction code for signals representing errors predicted on the time axis is encoded for transmission. Video frames under intraframe-encoding may be referred to as key frames, and the interval between key frames may not be constant. The remaining frames, i.e. non-key frames, may be referred to as Wyner-Ziv (WZ) frames.
The currently predominant DVC scheme is based on two important theorems, Slepian-Wolf and Wyner-Ziv theorems, in the field of information theory. In the DVC scheme, a Wyner-Ziv frame encoder processes Slepian-Wolf encoding on Wyner-Ziv frames of original motion video to be encoded to transmit resultant, encoded data, and a Wyner-Ziv frame decoder produces predictive frames with respect to the original video and uses the predictive frames together with the transmitted, encoded data to perform Slepian-Wolf decoding to reproduce the original motion video.
In the DVC scheme as described in Aaron et al., key frames are encoded and decoded in a conventional manner. Hence, description will be focused on encoding and decoding WZ frames.
As may be understood from FIG. 1 of Aaron et al., in the Wyner-Ziv frame encoder, WZ frames to be encoded are transformed into a transform coefficient domain, i.e. frequency domain, by a discrete cosine transform (DCT). The transformed data are then quantized for each component in the frequency domain by a 2Mk level quantizer. The quantized values qk are converted into bit string code, which will in turn be subjected to Slepian-Wolf encoding by a turbo encoder, for example, on a video frame-by-frame basis, i.e. an extract bit plane-by-plane basis. The resultant, encoded data contain parity bits, which will be temporarily stored in a buffer whereas the remaining bits will be discarded. This procedure on the bits is not explicitly illustrated in FIG. 1 of Aaron et al.
The Wyner-Ziv frame decoder produces predictive video frames by interpolation/extrapolation. The predictive frames are then DCT-transformed into a transform coefficient domain. The coefficients obtained by transforming are delivered as side information for respective components in the frequency domain to a Slepian-Wolf decoder, i.e. turbo decoder.
The Slepian-Wolf decoder requests the Wyner-Ziv frame encoder to transmit some of the parity bits temporarily stored. The Slepian-Wolf decoder then uses the transmitted parity bits as well as the side information to perform Slepian-Wolf decoding. If the decoding does not work adequately, the Slepian-Wolf decoder requests the Wyner-Ziv frame encoder again to additionally transmit some of the parity bits and then executes Slepian-Wolf decoding by means of the latest transmitted parity bits and the side information. This procedure is carried on until the decoding is satisfactory.
The decoded values, obtained by the Slepian-Wolf decoding, and the side information are used to reconstruct transform coefficients, which will then be inverse-transformed, or inverse DCT (IDCT)-transformed, to thereby obtain decoded video frames.
As stated in Aaron et al., it is known that, in the DVC scheme, the compression ratio is significantly influenced by how accurately the decoder produces predictive frames, or side information. The accurate generation of the side information would allow the compression ratio to be improved.
For that purpose, an attempt is proposed that the side information is updated as described in A. B. B. Adikari et al., “A Sequential Motion Compensation Refinement Technique for Distributed video coding of Wyner-Ziv frames,” ICIP 2006, USA (2006). In the solution described in Adikari et al., each time a decoder has decoded a bit plane, it reproduces a predictive frame by motion detection and compensation. Then, this newly produced side information is used to decode a next bit plane.
In the solution described in Adikari et al., side information, or motion video data on a predictive frame, is thus reproduced and updated, thereby allowing side information to be generated with higher accuracy. This can improve the accuracy of information bits on each bit plane so as to improve the compression ratio. It is, however, anticipated that motion detection and compensation have to be performed on each bit plane to be processed, thereby significantly increasing the amount of processing. The motion detection and compensation need extremely complicated calculation. Therefore, if a motion video decoder is implemented mainly by software, the number of steps for processing would significantly be increased. On the contrary, if a motion video decoder is implemented mainly by hardware, the decoder would be large in scale and complicated.